Software Defined Networking simplifies design, monitoring and management of next generation networks by segregating a legacy network into a centralized control plane and a remotely programmable data ...plane. The intelligent centralized SDN control plane controls behavior of forwarding devices in processing the incoming packets and provides a bird-eye view of entire network at a single central point. The centralized control provides network programmability and facilitates introduction of adaptive and automatic network control. The SDN control plane can be implemented by using following three deployment models: (i) physically centralized, in which a single SDN controller is configured for a network; (ii) physically distributed but logically centralized, wherein multiple SDN controllers are used to manage a network; and (iii) hybrid, in which both legacy distributed control and centralized SDN control coexist. This manuscript presents all these control plane architectures and discusses various SDN controllers supporting these architectures. We have analyzed more than forty SDN controllers in terms of following performance parameters: scalability, reliability, consistency and security. We have examined the mechanisms used by various SDN controllers to address the said performance parameters and have highlighted the pros and cons associated with each mechanism. In addition to it, this manuscript also highlights number of research challenges and open issues in different SDN control plane architectures.
Software defined networking (SDN) decouples the control plane from the data plane of forwarding devices. This separation provides several benefits, including the simplification of network management ...and control. However, due to a variety of reasons, such as budget constraints and fear of downtime, many organizations are reluctant to fully deploy SDN. Partially deploying SDN through the placement of a limited number of SDN devices among legacy (traditional) network devices, forms a so-called hybrid SDN network. While hybrid SDN networks provide many of the benefits of SDN and have a wide range of applications, they also pose several challenges. These challenges have recently been addressed in a growing body of literature on hybrid SDN network structures and protocols. This paper presents a comprehensive up-to-date survey of the research and development in the field of hybrid SDN networks. We have organized the survey into five main categories, namely hybrid SDN network deployment strategies, controllers for hybrid SDN networks, protocols for hybrid SDN network management, traffic engineering mechanisms for hybrid SDN networks, as well as testing, verification, and security mechanisms for hybrid SDN networks. We thoroughly survey the existing hybrid SDN network studies according to this taxonomy and identify gaps and limitations in the existing body of research. Based on the outcomes of the existing research studies as well as the identified gaps and limitations, we derive guidelines for future research on hybrid SDN networks.
Intelligent and autonomous SDN applications need to monitor the network state in order to take appropriate actions. In this letter, we compare the impact of active and passive network state ...collection methods on an SDN load-balancing application running at the controller. We do this comparison through: 1) the results of a mathematical model evaluation we derive for the SDN load-balancer, and 2) the results of a series of elaborate experiments we ran on our emulation setup. The results show that in case of low-variation traffic, the load-balancer with passive state collection performed better than the active one, which was confirmed by both model and experimental evaluation. However, the load-balancer with the active state collection was more resilient to the nature of the traffic load.
Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and ...programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded.
Having gained momentum from its promise of centralized control over distributed network architectures at bargain costs, software-defined Networking (SDN) is an ever-increasing topic of research. SDN ...offers a simplified means to dynamically control multiple simple switches via a single controller program, which contrasts with current network infrastructures where individual network operators manage network devices individually. Already, SDN has realized some extraordinary use cases outside of academia with companies, such as Google, AT&T, Microsoft, and many others. However, SDN still presents many research and operational challenges for government, industry, and campus networks. Because of these challenges, many SDN solutions have developed in an ad hoc manner that are not easily adopted by other organizations. Hence, this paper seeks to identify some of the many challenges where new and current researchers can still contribute to the advancement of SDN and further hasten its broadening adoption by network operators.
Software-defined networking (SDN) emerged as an attempt to introduce network innovations faster, and to radically simplify and automate the management of large networks. SDN traditionally leverages ...Open Flow as device-level abstraction. Since OpenFlow permits the programmer to "just" abstract a static flow-table, any stateful control and processing intelligence is necessarily delegated to the network controller. Motivated by the latency and signaling overhead that comes along with such a two-tiered SDN programming model, in the last couple of years several works have proposed innovative switch-level (data plane) programming abstractions capable to deploy some smartness directly inside the network switches, e.g., in the form of localized stateful flow processing. Furthermore, the possible inclusion of states and state maintenance primitives inside the switches is currently being debated in the OpenFlow standardization community itself. In this paper, after having provided the reader with a background on such emerging stateful SDN data plane proposals, we focus our attention on the security implications that data plane programmability brings about. Also via the identification of potential attack scenarios, we specifically highlight possible vulnerabilities specific to stateful in-switch processing (including denial of service and saturation attacks), which we believe should be carefully taken into consideration in the ongoing design of current and future proposals for stateful SDN data planes.
•An architecture for SDN-controlled transport networks is presented enabling real-time traffic monitoring directly within the transport SDN controller.•Machine learning algorithms have been ...integrated to forecast future link usage in network bandwidth management which allows to identify bottlenecks and to perform a better resource allocation.•The presented SDN controller is based on a microservice-based architecture which facilitates the implementation and scalability of the real-time traffic monitoring system within the SDN environment.
Network bandwidth is a scarce resource that network operators monitor to cope with future traffic demands and plan more transceiver and fibre deployments. The inclusion of Machine Learning permits the usage of traffic forecasting methods to predict future link usage. Typically, traffic analysis is performed offline due to the high computational load and difficulty of obtaining real-time data directly from the underlying network devices. To overcome these limitations, this paper presents and evaluates an architecture for SDN-controlled packetoptical transport networks to allow real-time traffic monitoring in the transport SDN controller. The presented SDN controller is based on a micro-service-based architecture, which facilitates the ease of deployment of the proposed solution. Four forecasting methods are proposed and evaluated against two topologies to select the most precise and the fastest among them.The algorithm random forest seems to be the most accurate forecasting future link usage with 79.98 % and 95.88 % accuracy and a reasonable fast speed when implemented it into two different topologies
Software-defined networking (SDN) architecture enables flexible and centralized network management from the controller, making it increasingly attractive in deploying telecommunications services. ...However, despite the many benefits of SDN, the vulnerabilities inherent in its architecture must be considered, and potential attacks must be discarded. When this occurs, not only the technical areas are interested in the source of the problem, but also the organizational areas, since attacks can violate terms of service and lead to legal actions. Despite the shared interest in cybersecurity event information, forensics and incident response processes often operate independently, impacting the root cause determination. Considering this concern, an architectural evolution for digital forensics and incident response (DFIR) management is introduced. This paper presents an event filtering model that serves as a trigger for initialing the DFIR process, which involves the detection of unusual traffic and unexpected behavior of SDN elements. The proposal applies artificial intelligence technology and showcases the performance of the model and the presentation of a proprietary dataset obtained from OpenFlow traffic.
Software-defined networking (SDN) has emerged as a new network paradigm that promises control/data plane separation and centralized network control. While these features simplify network management ...and enable innovative networking, they give rise to persistent concerns about reliability . The new paradigm suffers from the disadvantage that various network faults may consistently undermine the reliability of such a network, and such faults are often new and difficult to resolve with existing solutions. To ensure SDN reliability, fault management , which is concerned with detecting, localizing, correcting and preventing faults, has become a key component in SDN networks. Although many SDN fault management solutions have been proposed, we find that they often resolve SDN faults from an incomplete perspective which may result in side effects. More critically, as the SDN paradigm evolves, additional fault types are being exposed. Therefore, comprehensive reviews and constant improvements are required to remain on the leading edge of SDN fault management. In this paper, we present the first comprehensive and systematic survey of SDN faults and related management solutions identified through advancements in both the research community and industry. We apply a systematic classification of SDN faults, compare and analyze existing SDN fault management solutions in the literature, and conduct a gap analysis between solutions developed in an academic research context and practical deployments. The current challenges and emerging trends are also noted as potential future research directions. This paper aims to provide academic researchers and industrial engineers with a comprehensive survey with the hope of advancing SDN and inspiring new solutions.
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